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Overview

  • Founded Date 1926å¹´10月1æ—¥
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Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall parameters with 37B triggered for each token. To accomplish efficient reasoning and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training goal for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 surpasses other open-source models and attains efficiency similar to leading closed-source designs. Despite its excellent efficiency, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or carry out any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

– On top of the effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free technique for load balancing, which minimizes the efficiency deterioration that arises from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) objective and show it helpful to design performance. It can likewise be utilized for speculative decoding for inference velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 combined accuracy training structure and, for the very first time, verify the expediency and effectiveness of FP8 training on an extremely massive design.
– Through co-design of algorithms, frameworks, and hardware, we conquer the interaction bottleneck in cross-node MoE training, almost accomplishing full computation-communication overlap.
This significantly enhances our training efficiency and decreases the training expenses, allowing us to even more scale up the model size without additional overhead.
– At a cost-effective expense of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently greatest open-source base model. The subsequent training phases after pre-training need just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an innovative method to distill reasoning abilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series designs, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its thinking performance. Meanwhile, we also keep a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To guarantee ideal performance and flexibility, we have partnered with open-source neighborhoods and hardware suppliers to provide numerous ways to run the model in your area. For detailed guidance, take a look at Section 6: How_to Run_Locally.

For developers seeking to dive deeper, we suggest exploring README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is currently under active development within the community, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are shown in strong. Scores with a gap not surpassing 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the finest efficiency on the majority of criteria, particularly on math and code tasks. For more examination details, please examine our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well across all context window lengths approximately 128K.

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are examined in a setup that restricts the output length to 8K. Benchmarks including less than 1000 samples are checked several times using varying temperature settings to obtain robust final results. DeepSeek-V3 stands as the best-performing open-source model, and likewise displays competitive efficiency versus frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion examinations. For AlpacaEval 2.0, we utilize the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek’s main website: chat.deepseek.com

We likewise offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed in your area utilizing the following hardware and open-source neighborhood software:

DeepSeek-Infer Demo: We provide an easy and lightweight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 reasoning for regional and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 assistance coming quickly.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs by means of SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively embraced in our structure, we only offer FP8 weights. If you require BF16 weights for experimentation, you can use the supplied conversion script to carry out the transformation.

Here is an example of converting FP8 weights to BF16:

Hugging Face’s Transformers has not been directly supported yet. **

6.1 Inference with DeepSeek-Infer Demo (example only)

System Requirements

Note

Linux with Python 3.10 only. Mac and Windows are not supported.

Dependencies:

Model Weights & Preparation

First, clone our DeepSeek-V3 GitHub repository:

Navigate to the inference folder and install reliances noted in requirements.txt. Easiest method is to utilize a bundle manager like conda or uv to produce a new virtual environment and set up the dependencies.

Download the design weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.

Model Weights Conversion

Convert Hugging Face model weights to a particular format:

Run

Then you can talk with DeepSeek-V3:

Or batch reasoning on a provided file:

6.2 Inference with SGLang (suggested)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering cutting edge latency and throughput performance among open-source frameworks.

Notably, SGLang v0.4.1 completely supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely versatile and robust solution.

SGLang likewise supports multi-node tensor parallelism, enabling you to run this design on multiple network-connected makers.

Multi-Token Prediction (MTP) is in development, and progress can be tracked in the optimization plan.

Here are the launch guidelines from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

6.3 Inference with LMDeploy (advised)

LMDeploy, a flexible and high-performance reasoning and serving structure tailored for big language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation capabilities, effortlessly incorporating with PyTorch-based workflows.

For comprehensive detailed guidelines on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (advised)

TensorRT-LLM now supports the DeepSeek-V3 model, using accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be launched quickly. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 assistance through the following link to experience the brand-new functions directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (suggested)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers pipeline parallelism allowing you to run this model on several machines connected by networks. For in-depth assistance, please refer to the vLLM instructions. Please feel complimentary to follow the improvement strategy also.

6.6 Recommended Inference Functionality with AMD GPUs

In cooperation with the AMD team, we have accomplished Day-One assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For comprehensive guidance, please refer to the SGLang directions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has successfully adapted the BF16 variation of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the directions here.

7. License

This code repository is accredited under the MIT License. Making use of DeepSeek-V3 Base/Chat designs undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports business usage.

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